The project revolves around the application of computer vision techniques developed for the analysis of astronomical images to the field of quality assurance within the inspection of jet engine components used for the aerospace industry.
Aviation engines are required to be removed from the aircraft and overhauled after specific periods of operation, defined either by calendar time or their respective time between overhauls. During the overhaul, components such as compressor blades and turbine blades are removed from the engine, inspected and repaired. Utilising the computer vision techniques developed for astronomy it is expected that the inspection of the components can be improved via the automation of some key steps.
The student will join the astronomy group based in the Physics Department at the University of Sheffield. The group has developed a novel machine-learning-based technique to “spot-the-difference” between two images of the same patch of the night sky to identify those sources – such as stars or asteroids – which have become brighter or fainter between when the two images were taken. This is part of a broader interest in computer vision techniques that the group, and astronomy research in general, currently has.
The student will spend a year at the University of Sheffield learning how to use and further develop the “spot-the-difference” algorithm, together with more general computer vision techniques. They will then take what they have learned in Sheffield to A*STAR in Singapore to work with researchers in industry to investigate whether the “spot-the-difference” algorithm can be applied to identify defects in aeroengine blades. The machine vision algorithm will be extended to incorporate 3D spatial analysis to mark out defects and to determine the required subsequent machining for material addition. The developed techniques can be applied to other high value components such as shafts and engine components.
This is a fully-funded four-year PhD project. As this project is a collaboration between researchers based in Sheffield and Singapore, successful applicants will be required to spend at least one and up to two years of their PhD in Singapore, with the other years based in Sheffield. Funding is available for the flights to Singapore.
- Academic requirements: applicants should have, or expect to achieve, a first or upper second class UK honours degree (or equivalent qualifications gained outside the UK) in an appropriate STEM area of study.
- Applicants should be registering on their first year of study with the University for 2023/24 on an eligible programme of doctoral study.
- Candidates must qualify for Home fee status.
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